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chrispiech · Oct 31, 2023 · 63baca5 · 63baca5
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diff --git a/chapters/examples/.DS_Store b/chapters/examples/.DS_Store
diff --git a/chapters/part1/bayes_theorem/index.html b/chapters/part1/bayes_theorem/index.html
@@ -85,7 +85,7 @@
 </p>
 
 <p>A common scenario for applying the Bayes' Rule formula is when you want to know the probability of
-something “unobservable” given an “observed” event. For example, you want to know the probability that a
+something “unobservable” given an “observed” event! For example, you want to know the probability that a
 student understands a concept, given that you observed them solving a particular problem. It turns out it is
 much easier to first estimate the probability that a student can solve a problem given that they understand the
 concept and then to apply Bayes' Theorem. Intuitively, you can think about this as updating a belief given

diff --git a/en/ProbabilityForComputerScientists.pdf b/en/ProbabilityForComputerScientists.pdf
diff --git a/en/part1/bayes_theorem/index.html b/en/part1/bayes_theorem/index.html
@@ -434,7 +434,7 @@
 </p>
 
 <p>A common scenario for applying the Bayes' Rule formula is when you want to know the probability of
-something “unobservable” given an “observed” event. For example, you want to know the probability that a
+something “unobservable” given an “observed” event! For example, you want to know the probability that a
 student understands a concept, given that you observed them solving a particular problem. It turns out it is
 much easier to first estimate the probability that a student can solve a problem given that they understand the
 concept and then to apply Bayes' Theorem. Intuitively, you can think about this as updating a belief given

diff --git a/print/hash_values.json b/print/hash_values.json
@@ -0,0 +1,81 @@
+{
+ "../en/intro/notation/index.html": "80ce2a0c31d5958062a9e36ee6778986",
+ "../en/intro/core_probability_ref/index.html": "40c849fe059aa68d3e46322d59e0b6ee",
+ "../en/intro/all_distributions/index.html": "3041852c774c3bf1855bb94aa96b7e5e",
+ "../en/intro/python/index.html": "ff192d5c8732ad219e111d3ef4bdc26e",
+ "../en/intro/calculators/index.html": "15ad7186354bc063b6926a3e26abc0d3",
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+ "../en/part1/combinatorics/index.html": "980e8d6fd32d78749d18397334b01c05",
+ "../en/part1/probability/index.html": "98412776cf5aab1c729c9296d321ab1a",
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+ "../en/part1/cond_prob/index.html": "e190909f054bea9c5520c826afa9a4ef",
+ "../en/part1/independence/index.html": "69809940c0504eab3d83a00335f0c17e",
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+ "../en/part2/rvs/index.html": "0b0a02f4fd4d2eedb364067bb42a4017",
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+ "../en/part2/bernoulli/index.html": "5d900a9189ee40704652dfc8a98829a6",
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+ "../en/part2/exponential/index.html": "293c9bf211ab18aaa97405d8563aebcb",
+ "../en/part2/normal/index.html": "104742361b8d1b301f65911533aeca17",
+ "../en/part2/binomial_approx/index.html": "fbc8904aa5fffd864d8b93522e581f2d",
+ "../en/part3/joint/index.html": "c4bf9a82dce0bff524aa4d346fb24630",
+ "../en/part3/multinomial/index.html": "4d1efa18cda9604eefd376e658700e65",
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+ "../en/part3/bayesian_networks/index.html": "ab652d1ca64d12553562a0a69cdedad0",
+ "../en/part3/independent_vars/index.html": "cb0e98b009940291baa2df472d9a5dfd",
+ "../en/part3/correlation/index.html": "28686b4e41b6a7455fcab09cf622685b",
+ "../en/part3/computational_inference/index.html": "e64e0329ce941690a3fa0010af0972bd",
+ "../en/part4/beta/index.html": "011f60d1b84968a61fcc383324fc1ab1",
+ "../en/part4/summation_vars/index.html": "e417c194a7db4c82256794e83de4e015",
+ "../en/part4/clt/index.html": "9acf281e179adb877faa125c6357b33f",
+ "../en/part4/samples/index.html": "447a1388c39b9dd30ef10439f31c6283",
+ "../en/part4/bootstrapping/index.html": "61ffde20ca6259d9498b772c81822bd3",
+ "../en/part4/algorithmic_analysis/index.html": "a144057c9e461828eb0810da20def509",
+ "../en/part5/parameter_estimation/index.html": "663a7b969e328c599284e681bb061cbb",
+ "../en/part5/mle/index.html": "ccf756bb2e6fb28115cbcae305439f69",
+ "../en/part5/map/index.html": "bd2ba4199c1cfe848feab49a79722938",
+ "../en/part5/machine_learning/index.html": "52d9f4d520e100b8d9cc9f09950c42ea",
+ "../en/part5/naive_bayes/index.html": "1f14ae36921d1613c1d573af95f51764",
+ "../en/part5/log_regression/index.html": "378d0f59da28c00a45d8753ecefbf3a9",
+ "../en/examples/enigma/index.html": "afd97e5dacb9ce1d5c99dc57413a30a4",
+ "../en/examples/serendipity/index.html": "7ed5e95e76e022f73301508dedc50e3b",
+ "../en/examples/random_shuffles/index.html": "9af14913ec75f347f8dacff9bc7502de",
+ "../en/examples/counting_graphs/index.html": "17e2d92a722d3f352adfa5de066bf9c8",
+ "../en/examples/bacteria_evolution/index.html": "c0e54908b2e3b953a470db76bbab97d5",
+ "../en/examples/100_binomial_problems/index.html": "931319539da54851ed9776edd0b36f65",
+ "../en/examples/winning_series/index.html": "e2fdec78561e68b51b4369ab235cb4b8",
+ "../en/examples/approximate_counting/index.html": "a8435db01dfb766177412971de483fb6",
+ "../en/examples/jury/index.html": "c23c32d6ec53946077ad41821d40275f",
+ "../en/examples/prob_baby_delivery/index.html": "43b5904dcb4a64344f203053fdd72736",
+ "../en/examples/grading_eye_inflammation/index.html": "57eefdc10ed61dc20ea115e9856bd1f5",
+ "../en/examples/grades_not_normal/index.html": "aeb3700aaf5cdb90d9373b258ffbaa18",
+ "../en/examples/curse_of_dimensionality/index.html": "ec7d406a6878fca840f8d842eb0f950b",
+ "../en/examples/algorithmic_art/index.html": "151ae1632171939057b639f8de6cc339",
+ "../en/examples/fairness/index.html": "81c8368b171eba43521024aa7e5b9a40",
+ "../en/examples/federalist/index.html": "a04081aab1d7fd3775f00c128387449c",
+ "../en/examples/name2age/index.html": "2d8018282c547cc510e59c3df2175185",
+ "../en/examples/bayesian_carbon_dating/index.html": "b0e770ebeb3cdeabfd4f0d2b6e07949b",
+ "../en/examples/digital_vision_test/index.html": "4984c76554fcff2a38a6bda8d17c06c3",
+ "../en/examples/bridge_distribution/index.html": "3d4e0500b2f49a5bcd5aa3a06c2a2220",
+ "../en/examples/bayesian_viral_load_test/index.html": "5484a94acadef7ae6dc20a09e468135e",
+ "../en/examples/dart_logo/index.html": "c140d81c37f03ca7e26d07a3d8467e48",
+ "../en/examples/tracking_in_2D/index.html": "fbc47eeb24db26e94586cdd95313710d",
+ "../en/examples/thompson/index.html": "d79bc3b51a6cde0dc97eaff6b9513867",
+ "../en/examples/night_sight/index.html": "9b37b6c30e195ac54af6053085b978dd",
+ "../en/examples/p_hacking/index.html": "8b1141fa7ac25b4fbdfca46ef604c556",
+ "../en/examples/differential_privacy/index.html": "4cfb8805a4bb42d908aaaf9efe179ef2",
+ "../en/examples/mle_demo/index.html": "b26b666d3b7124cbedc81cbd819c6c99",
+ "../en/examples/mle_pareto/index.html": "408438fe6a9a56c890605636eaf46fce",
+ "../en/examples/mixture_models/index.html": "55f6390b3ce3ed1c1846af3f887a6129"
+}
diff --git a/print/pdfs/100_binomial_problems.pdf b/print/pdfs/100_binomial_problems.pdf
diff --git a/print/pdfs/algorithmic_analysis.pdf b/print/pdfs/algorithmic_analysis.pdf
diff --git a/print/pdfs/algorithmic_art.pdf b/print/pdfs/algorithmic_art.pdf
diff --git a/print/pdfs/all_distributions.pdf b/print/pdfs/all_distributions.pdf
diff --git a/print/pdfs/approximate_counting.pdf b/print/pdfs/approximate_counting.pdf
diff --git a/print/pdfs/bacteria_evolution.pdf b/print/pdfs/bacteria_evolution.pdf
diff --git a/print/pdfs/bayes_theorem.pdf b/print/pdfs/bayes_theorem.pdf
diff --git a/print/pdfs/bayesian_carbon_dating.pdf b/print/pdfs/bayesian_carbon_dating.pdf
diff --git a/print/pdfs/bayesian_networks.pdf b/print/pdfs/bayesian_networks.pdf
diff --git a/print/pdfs/bayesian_viral_load_test.pdf b/print/pdfs/bayesian_viral_load_test.pdf
diff --git a/print/pdfs/bernoulli.pdf b/print/pdfs/bernoulli.pdf
diff --git a/print/pdfs/beta.pdf b/print/pdfs/beta.pdf
diff --git a/print/pdfs/binomial.pdf b/print/pdfs/binomial.pdf
diff --git a/print/pdfs/binomial_approx.pdf b/print/pdfs/binomial_approx.pdf
diff --git a/print/pdfs/bootstrapping.pdf b/print/pdfs/bootstrapping.pdf
diff --git a/print/pdfs/bridge_distribution.pdf b/print/pdfs/bridge_distribution.pdf
diff --git a/print/pdfs/calculators.pdf b/print/pdfs/calculators.pdf
diff --git a/print/pdfs/clt.pdf b/print/pdfs/clt.pdf
diff --git a/print/pdfs/combinatorics.pdf b/print/pdfs/combinatorics.pdf
diff --git a/print/pdfs/computational_inference.pdf b/print/pdfs/computational_inference.pdf
diff --git a/print/pdfs/cond_prob.pdf b/print/pdfs/cond_prob.pdf
diff --git a/print/pdfs/continuous.pdf b/print/pdfs/continuous.pdf
diff --git a/print/pdfs/continuous_joint.pdf b/print/pdfs/continuous_joint.pdf
diff --git a/print/pdfs/core_probability_ref.pdf b/print/pdfs/core_probability_ref.pdf
diff --git a/print/pdfs/correlation.pdf b/print/pdfs/correlation.pdf
diff --git a/print/pdfs/counting.pdf b/print/pdfs/counting.pdf
diff --git a/print/pdfs/counting_graphs.pdf b/print/pdfs/counting_graphs.pdf
diff --git a/print/pdfs/curse_of_dimensionality.pdf b/print/pdfs/curse_of_dimensionality.pdf
diff --git a/print/pdfs/dart_logo.pdf b/print/pdfs/dart_logo.pdf
diff --git a/print/pdfs/differential_privacy.pdf b/print/pdfs/differential_privacy.pdf
diff --git a/print/pdfs/digital_vision_test.pdf b/print/pdfs/digital_vision_test.pdf
diff --git a/print/pdfs/enigma.pdf b/print/pdfs/enigma.pdf
diff --git a/print/pdfs/equally_likely.pdf b/print/pdfs/equally_likely.pdf
diff --git a/print/pdfs/expectation.pdf b/print/pdfs/expectation.pdf
diff --git a/print/pdfs/exponential.pdf b/print/pdfs/exponential.pdf
diff --git a/print/pdfs/fairness.pdf b/print/pdfs/fairness.pdf
diff --git a/print/pdfs/federalist.pdf b/print/pdfs/federalist.pdf
diff --git a/print/pdfs/grades_not_normal.pdf b/print/pdfs/grades_not_normal.pdf
diff --git a/print/pdfs/grading_eye_inflammation.pdf b/print/pdfs/grading_eye_inflammation.pdf
diff --git a/print/pdfs/independence.pdf b/print/pdfs/independence.pdf
diff --git a/print/pdfs/independent_vars.pdf b/print/pdfs/independent_vars.pdf
diff --git a/print/pdfs/inference.pdf b/print/pdfs/inference.pdf
diff --git a/print/pdfs/joint.pdf b/print/pdfs/joint.pdf
diff --git a/print/pdfs/jury.pdf b/print/pdfs/jury.pdf
diff --git a/print/pdfs/law_total.pdf b/print/pdfs/law_total.pdf
diff --git a/print/pdfs/log_probabilities.pdf b/print/pdfs/log_probabilities.pdf
diff --git a/print/pdfs/log_regression.pdf b/print/pdfs/log_regression.pdf
diff --git a/print/pdfs/machine_learning.pdf b/print/pdfs/machine_learning.pdf
diff --git a/print/pdfs/many_flips.pdf b/print/pdfs/many_flips.pdf
diff --git a/print/pdfs/map.pdf b/print/pdfs/map.pdf
diff --git a/print/pdfs/mixture_models.pdf b/print/pdfs/mixture_models.pdf
diff --git a/print/pdfs/mle.pdf b/print/pdfs/mle.pdf
diff --git a/print/pdfs/mle_demo.pdf b/print/pdfs/mle_demo.pdf
diff --git a/print/pdfs/mle_pareto.pdf b/print/pdfs/mle_pareto.pdf
diff --git a/print/pdfs/multinomial.pdf b/print/pdfs/multinomial.pdf
diff --git a/print/pdfs/naive_bayes.pdf b/print/pdfs/naive_bayes.pdf
diff --git a/print/pdfs/name2age.pdf b/print/pdfs/name2age.pdf
diff --git a/print/pdfs/night_sight.pdf b/print/pdfs/night_sight.pdf
diff --git a/print/pdfs/normal.pdf b/print/pdfs/normal.pdf
diff --git a/print/pdfs/notation.pdf b/print/pdfs/notation.pdf
diff --git a/print/pdfs/p_hacking.pdf b/print/pdfs/p_hacking.pdf
diff --git a/print/pdfs/parameter_estimation.pdf b/print/pdfs/parameter_estimation.pdf
diff --git a/print/pdfs/pmf.pdf b/print/pdfs/pmf.pdf
diff --git a/print/pdfs/poisson.pdf b/print/pdfs/poisson.pdf
diff --git a/print/pdfs/prob_and.pdf b/print/pdfs/prob_and.pdf
diff --git a/print/pdfs/prob_baby_delivery.pdf b/print/pdfs/prob_baby_delivery.pdf
diff --git a/print/pdfs/prob_or.pdf b/print/pdfs/prob_or.pdf
diff --git a/print/pdfs/probability.pdf b/print/pdfs/probability.pdf
diff --git a/print/pdfs/python.pdf b/print/pdfs/python.pdf
diff --git a/print/pdfs/random_shuffles.pdf b/print/pdfs/random_shuffles.pdf
diff --git a/print/pdfs/rvs.pdf b/print/pdfs/rvs.pdf
diff --git a/print/pdfs/samples.pdf b/print/pdfs/samples.pdf
diff --git a/print/pdfs/serendipity.pdf b/print/pdfs/serendipity.pdf
diff --git a/print/pdfs/summation_vars.pdf b/print/pdfs/summation_vars.pdf
diff --git a/print/pdfs/thompson.pdf b/print/pdfs/thompson.pdf
diff --git a/print/pdfs/titlepage.pdf b/print/pdfs/titlepage.pdf
diff --git a/print/pdfs/tracking_in_2D.pdf b/print/pdfs/tracking_in_2D.pdf
diff --git a/print/pdfs/uniform.pdf b/print/pdfs/uniform.pdf
diff --git a/print/pdfs/variance.pdf b/print/pdfs/variance.pdf
diff --git a/print/pdfs/winning_series.pdf b/print/pdfs/winning_series.pdf
diff --git a/print/printbook.py b/print/printbook.py
@@ -1,11 +1,13 @@
 import hjson
+import json
 import PyPDF2
 import pdfgenerator
 import re
 import os
 from tqdm import tqdm
+import hashlib
 
-# def create_pdfs():
+
 # Opening JSON file
 f = open("../bookOutline.hjson")
 # returns JSON object as a dictionary
@@ -31,77 +33,109 @@
 
 # get pdf_name and pdf_link for book from bookOutline and store in pdf_files
 pdf_files = {}
+# load the hash values
+# if the file does not exist, hash_values is {}
+if not os.path.exists('hash_values.json'):
+ hash_values = {}
+else:
+ hash_values = json.load(open('hash_values.json', 'r'))
+
+def print_page(part, page):
+ pdf_name = page + '.pdf'
+ pdf_link = base + part + '/' + page
+ file_link = f'../en/{part}/{page}/index.html'
+ raw_file_html = open(file_link, 'r').read()
+ hash = compute_md5(raw_file_html)
+ if file_link in hash_values.keys() and hash_values[file_link] == hash:
+ return
+ # store pdf_name and title
+ # generate pdf file
+ print(pdf_link)
+ pdf_file = pdfgenerator.PdfGenerator([pdf_link]).main()
+ # save pdf to file
+ with open(os.path.join('pdfs', pdf_name), "wb") as outfile:
+ outfile.write(pdf_file[0].getbuffer())
+ save_hash_value(hash, file_link)
 
-for part in tqdm(data, desc="Outer Loop"):
- pdf_files[part] = {'sections':{}}
- for page in data[part]['sections']:
- title = data[part]['sections'][page]
- pdf_name = page + '.pdf'
- pdf_link = base + part + '/' + page 
- # store pdf_name and title
- pdf_files[part]['sections'][pdf_name] = title
- # generate pdf file
- pdf_file = pdfgenerator.PdfGenerator([pdf_link]).main()
- # save pdf to file
- with open(os.path.join('pdfs', pdf_name), "wb") as outfile:
- outfile.write(pdf_file[0].getbuffer())
- # print(pdf_name)
- if 'examples' in data[part].keys():
- pdf_files[part]['examples'] = {}
- for page in data[part]['examples']:
- title = data[part]['examples'][page]
+def print_book():
+ for part in tqdm(data, desc="Sections"):
+ pdf_files[part] = {'sections':{}}
+ for page in data[part]['sections']:
+ print_page(part, page)
+
+ # save for table of contents
+ title = data[part]['sections'][page]
  pdf_name = page + '.pdf'
- pdf_link = base + 'examples' + '/' + page 
- # store pdf_name and title
- pdf_files[part]['examples'][pdf_name] = title
- # generate pdf file
- pdf_file = pdfgenerator.PdfGenerator([pdf_link]).main()
- # save pdf to file
- with open(os.path.join('pdfs', pdf_name), "wb") as outfile:
- outfile.write(pdf_file[0].getbuffer())
- # print(pdf_name)
-
-# Output PDF file name
-output_pdf = "../en/ProbabilityForComputerScientists.pdf"
-
-# Create a PDF file writer object
-pdf_writer = PyPDF2.PdfWriter()
-
-# add title page
-pdf_writer.append(os.path.join('pdfs', title_name))
-
-page_num = 1 
-for part in pdf_files:
- title = data[part]['title']
- if title is None:
- title = "Introduction"
- # create outline for parts
- part_outline = pdf_writer.add_outline_item(title, page_num)
- # add pdf files to table of contents and book
- for pdf_file, title in pdf_files[part]['sections'].items():
- # Open the pdf
- pdf_reader = PyPDF2.PdfReader(open(os.path.join('pdfs', pdf_file), "rb"))
- # Create an outline item for the pdf
- pdf_outline = pdf_writer.add_outline_item(title, page_num, parent=part_outline)
- # add pdf file to book pdf 
- pdf_writer.append(os.path.join('pdfs', pdf_file))
- # start of next pdf 
- page_num += len(pdf_reader.pages)
- if 'examples' in pdf_files[part].keys():
- # create outline for examples
- examples_outline = pdf_writer.add_outline_item('Applications', page_num, parent=part_outline)
+ pdf_files[part]['sections'][pdf_name] = title
+ if 'examples' in data[part].keys():
+ pdf_files[part]['examples'] = {}
+ for page in data[part]['examples']:
+ print_page('examples', page)
+
+ # save for table of contents
+ title = data[part]['examples'][page]
+ pdf_name = page + '.pdf'
+ pdf_files[part]['examples'][pdf_name] = title
+
+
+ # Output PDF file name
+ output_pdf = "../en/ProbabilityForComputerScientists.pdf"
+
+ # Create a PDF file writer object
+ pdf_writer = PyPDF2.PdfWriter()
+
+ # add title page
+ pdf_writer.append(os.path.join('pdfs', title_name))
+
+ page_num = 1 
+ for part in pdf_files:
+ title = data[part]['title']
+ if title is None:
+ title = "Introduction"
+ # create outline for parts
+ part_outline = pdf_writer.add_outline_item(title, page_num)
  # add pdf files to table of contents and book
- for pdf_file, title in pdf_files[part]['examples'].items():
+ for pdf_file, title in pdf_files[part]['sections'].items():
  # Open the pdf
  pdf_reader = PyPDF2.PdfReader(open(os.path.join('pdfs', pdf_file), "rb"))
  # Create an outline item for the pdf
- pdf_outline = pdf_writer.add_outline_item(title, page_num, parent=examples_outline)
+ pdf_outline = pdf_writer.add_outline_item(title, page_num, parent=part_outline)
+ # add pdf file to book pdf 
  pdf_writer.append(os.path.join('pdfs', pdf_file))
  # start of next pdf 
  page_num += len(pdf_reader.pages)
+ if 'examples' in pdf_files[part].keys():
+ # create outline for examples
+ examples_outline = pdf_writer.add_outline_item('Applications', page_num, parent=part_outline)
+ # add pdf files to table of contents and book
+ for pdf_file, title in pdf_files[part]['examples'].items():
+ # Open the pdf
+ pdf_reader = PyPDF2.PdfReader(open(os.path.join('pdfs', pdf_file), "rb"))
+ # Create an outline item for the pdf
+ pdf_outline = pdf_writer.add_outline_item(title, page_num, parent=examples_outline)
+ pdf_writer.append(os.path.join('pdfs', pdf_file))
+ # start of next pdf 
+ page_num += len(pdf_reader.pages)
+
+ # Save the merged PDF with the TOC
+ with open(output_pdf, "wb") as output_file:
+ pdf_writer.write(output_file)
+
+ print(f"Merged PDF with Table of Contents saved as {output_pdf}")
+
+def save_hash_value(hash, file_link):
+
+ # store the hash value
+ hash_values[file_link] = hash
+
+ # save the hash values to json
+ with open('hash_values.json', 'w') as fp:
+ json.dump(hash_values, fp, indent=4)
 
-# Save the merged PDF with the TOC
-with open(output_pdf, "wb") as output_file:
- pdf_writer.write(output_file)
+def compute_md5(text):
+ m = hashlib.md5()
+ m.update(text.encode('utf-8'))
+ return m.hexdigest()
 
-print(f"Merged PDF with Table of Contents saved as {output_pdf}")
+if __name__ == "__main__":
+ print_book()
diff --git a/searchIndex.json b/searchIndex.json